P. Daniel Wright

A Survey of Operations Research Models and Applications in Homeland Security

Operations research has had a long and distinguished history of work in emergency preparedness and response, airline security, transportation of hazardous materials, and threat and vulnerability analysis. Since the attacks of September 11, 2001 and the formation of the US Department of Homeland Security, these topics have been gathered under the broad umbrella of homeland security. In addition, other areas of OR applications in homeland security are evolving, such as border and port security, cyber security, and critical infrastructure protection. The opportunities for operations researchers to contribute to homeland security remain numerous.

Strategies for Addressing the Nursing Shortage: Coordinated Decision Making and Workforce Flexibility

In this article, we present strategies to help combat the U.S. nursing shortage. Key considerations include providing an attractive work schedule and work environment—critical issues for retaining existing nurses and attracting new nurses to the profession—while at the same time using the set of available nurses as effectively as possible. Based on these ideas, we develop a model that takes advantage of coordinated decision making when managing a flexible workforce. The model coordinates scheduling, schedule adjustment, and agency nurse decisions across various nurse labor pools, each of differing flexibility levels, capabilities, and costs, allowing a much more desirable schedule to be constructed. Our primary findings regarding coordinated decision making and how it can be used to help address the nursing shortage include (i) labor costs can be reduced substantially because, without coordination, labor costs on average are 16.3% higher based on an actual hospital setting, leading to the availability of additional funds for retaining and attracting nurses, (ii) simultaneous to this reduction in costs, more attractive schedules can be provided to the nurses in terms of less overtime and fewer undesirable shifts, and (iii) the use of agency nurses can help avoid overtime for permanent staff with only a 0.7% increase in staffing costs. In addition, we estimate the cost of the shortage for a typical U.S. hospital from a labor cost perspective and show how that cost can be reduced when managers coordinate.

Simultaneous use of customer, product and inventory information in dynamic product promotion

Currently, retail data are both accessible and plentiful while the retail space has become increasingly competitive. When combined with technology like mobile computing and low cost analytic techniques, data can now be leveraged by companies to dynamically offer individualised promotions in real time. This paper considers the relative value of three retail information elements which can be used by retailers to dynamically identify a subset of product offerings to promote to their customers. The retail information elements considered are: (a) product markup, (b) customer preference estimates gleaned from purchase history and (c) retailer inventory positions. The importance of each element is evaluated singularly and in combination as is their effect on promotion success, inventory costs and average markup. Computational results show that, on average, dynamic promotion policies incorporating all retail information elements can increase expected profit by 14.5% over policies that consider only customer preference and by 8.4–9.1% over policies that consider only product margin or inventory. Results demonstrate that customer preference information alone does little to improve performance but provides substantial synergistic benefits when combined with either inventory or markup information elements. The most information intensive dynamic promotion policy is then extended to include price as a decision variable.

In-Store Pickup and Returns for a Dual Channel Retailer

The Internet and technology have changed how products are sold and delivered to consumers. Today, the most significant growth in online retailing comes from multichannel retailers that sell products both in stores and over the Internet. Recently, these retail/e-tail organizations have attempted to leverage their “brick” locations by allowing customers to pick up or return orders purchased online at retail store locations. Such options let online customers avoid both long carrier lead times and high shipping costs. However, these options come at a cost to the retailer. This paper develops a mathematical model for analytically examining the cost and value of providing in-store pickup and return options in multi-echelon retail/e-tail organizations. In this light, the model determines the optimal subset of a retailer/e-tailers stores that should be set up to handle in-store pickups and online returns under stochastic channel demands. Computational results show that optimizing the set of pickup and return locations can reduce system cost by up to 20% on average over arbitrarily enabling all stores with Internet pickup/return capabilities, and firms can substantially increase customer value while maintaining cost minimization as an important selection criterion in choosing pickup and return locations.

Eli Lilly and Company Uses Integer Programming to Form Volunteer Teams in Impoverished Countries

Each year, Eli Lilly and Company Lilly offers its worldwide employees the opportunity to participate in paid volunteer teams serving communities in impoverished countries. The company’s Connecting Hearts Abroad service program gives employees a unique opportunity to take part in service trips aimed at improving global health. Lilly annually offers about 23 trips, enabling employees to serve some of the world’s most resource-constrained regions where people lack basic resources or access to healthcare. A selection committee at Lilly manually forms volunteer teams from a large pool of applicants. Unfortunately, the manual selection process is time consuming and often fails to meet employee preference or adequately represent some applicant groups. This paper describes how we developed a mathematical programming model to improve Lilly’s process of volunteer selection. We incorporated the model into a decision support tool that assigns applicants to volunteer assignments and maximizes the chosen volunteers’ preferences under constraints that help ensure fair team compositions. Running the model against the prior year’s applicant data pool took less than two minutes to configure teams such that all volunteers received their first-choice assignment. The automated decision support system also provides a more consistent method of configuring teams that appears fair to the applicants.